TY - GEN
T1 - Generation of Bases for Classification in the Bio-inspired Layered Networks
AU - Ishii, Naohiro
AU - Iwata, Kazunori
AU - Matsuo, Tokuro
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Machine learning, deep learning and neural networks are extensively developed in many fields. As the function of cortical neural model, a sparse coding has been studied which is based on the bases functions of input stimulus. In this paper, it is shown that the bio-inspired networks are useful for the explanation of network functions. First, the asymmetric network with nonlinear functions is created based on the bio-inspired retinal network. They have orthogonal properties useful for features classification and processing. Second, it is shown that the asymmetric network is superior to the conventional symmetric network in the classification performance. Further, the asymmetric network is extended to the layered networks, which are also generated on the bio-inspired model of brain cortex. In the extended asymmetric layered networks, the higher dimensional orthogonal bases are created. To improve the classification performance, the bases replacements are performed in the layered networks. It is shown the bases replacements in the layered networks improve classification performance in both asymmetric and symmetric networks.
AB - Machine learning, deep learning and neural networks are extensively developed in many fields. As the function of cortical neural model, a sparse coding has been studied which is based on the bases functions of input stimulus. In this paper, it is shown that the bio-inspired networks are useful for the explanation of network functions. First, the asymmetric network with nonlinear functions is created based on the bio-inspired retinal network. They have orthogonal properties useful for features classification and processing. Second, it is shown that the asymmetric network is superior to the conventional symmetric network in the classification performance. Further, the asymmetric network is extended to the layered networks, which are also generated on the bio-inspired model of brain cortex. In the extended asymmetric layered networks, the higher dimensional orthogonal bases are created. To improve the classification performance, the bases replacements are performed in the layered networks. It is shown the bases replacements in the layered networks improve classification performance in both asymmetric and symmetric networks.
KW - asymmetric and symmetric networks
KW - classification performance of networks
KW - extended layered networks
KW - generation of orthogonal bases
KW - replacement of bases
UR - https://www.scopus.com/pages/publications/85164008550
UR - https://www.scopus.com/pages/publications/85164008550#tab=citedBy
U2 - 10.1007/978-3-031-34204-2_10
DO - 10.1007/978-3-031-34204-2_10
M3 - Conference contribution
AN - SCOPUS:85164008550
SN - 9783031342035
T3 - Communications in Computer and Information Science
SP - 110
EP - 120
BT - Engineering Applications of Neural Networks - 24th International Conference, EAAAI/EANN 2023, Proceedings
A2 - Iliadis, Lazaros
A2 - Maglogiannis, Ilias
A2 - Alonso, Serafin
A2 - Jayne, Chrisina
A2 - Pimenidis, Elias
PB - Springer Science and Business Media Deutschland GmbH
T2 - 24th International Conference on Engineering Applications of Neural Networks, EANN 2023
Y2 - 14 June 2023 through 17 June 2023
ER -